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Consuming patients’ days: Time spent on ambulatory visits by persons with cancer.

2023· article· en· W4388202341 on OpenAlexaff
Alexander K. Tsai, Manju George, Sydney Davis, Patricia Jewett, Rachel I. Vogel, Ishani Ganguli, Christopher M. Booth, Stacie B. Dusetzina, Gabrielle B. Rocque, Anne Blaes, Arjun Gupta

Bibliographic record

VenueJCO Oncology Practice · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsQueen's University
Fundersnot available
KeywordsAmbulatoryMedicineAmbulatory careCancerTravel timeEmergency medicineFamily medicineHealth careSurgeryInternal medicine

Abstract

fetched live from OpenAlex

324 Background: Health care-related time burdens faced by persons with cancer can be substantial. In qualitative work, patients report that ‘’seemingly short clinic visits (e.g., a supposed 10-minute blood draw) often turn into all-day affairs.” We sought to estimate the total time spent (home-to-home) by patients with cancer on ambulatory services at an urban cancer center. Methods: We conducted a retrospective study of all patients with cancer scheduled for any ambulatory care (e.g., laboratory testing, imaging, procedures, infusions, clinician visits) at a Midwestern academic cancer center during a randomly selected week (Monday-Friday) in January 2023. We extracted sociodemographics, clinical characteristics, and services received from the electronic medical record. The primary exposure was the ambulatory care type or combination received by a patient on a day (e.g., clinician visit only, labs and infusion, etc.). Using data from the Real-Time Location System (RTLS) badge that patients wear from clinic entry to exit (standard of care to optimize clinic workflow), we tracked time spent in each patient location (e.g., exam room) and in the clinic overall. We estimated round-trip travel times using home and clinic zip codes and SAS/Google Maps. We calculated median parking time by directly observing 20 random patients between the clinic and their vehicles (e.g., parking ramp or valet). We calculated and summarized clinic and total (clinic + travel + parking) times for different ambulatory care types. Results: We analyzed 468 encounters by 436 unique patients (median age, 64 years, 52% women, 82% white race, 19% breast cancer). Median (IQR) clinic time per encounter was 118 (76-200) minutes. The median (IQR) round-trip driving distance and travel time was 33 (21-53) miles and 50 (36-68) minutes. The median parking time was 8 (6-10) minutes. The median (IQR) total time was 189 (136-277) minutes. Table 1 presents the times by type(s) of ambulatory services. Conclusions: In this single-center retrospective study, pursuing ambulatory cancer care on a given day required patients (and their accompanying care partners) to commit several hours to their care. These findings highlight the need to decrease care time demands on patients to mitigate time toxicity. Accounting for opportunity time costs and the coordination of other daily activities around ambulatory care, these results support our previously proposed practical measure of the time burdens of cancer care—that any day with in-person healthcare contact may represent a ‘’lost day.”[Table: see text]

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.365
Teacher spread0.335 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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